Prominent examples of these explanatory mechanisms are Counterfactual Explanations and Semifactual Explanations [1]}.
Counterfactual explanations show a version of the input data that is altered just enough to change an AI's decision.
By doing so, the user is shown not only which features are relevant to the decision, but more importantly, how they would need to be changed to result in a different decision of the AI.
Semifactual explanations follow a similar principle, but they modify the relevant features of the input data to an extent that the AI's decision does not change just yet.
